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Multimodal trust based recommender system with machine learning approaches for movie recommendation

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Abstract

Recommender system (RS) are a type of suggestion to the information overload problem suffered by user of websites that allow the rating of particular item. The movie RS are one of the most efficient, useful, and widespread applications for individual to watch movie with minimum decision time. Many attempts made by the researchers to solve these issues like watching movie, purchasing book etc., through RS, whereas most of the study fails to address cold start problem, data sparsity and malicious attacks. This study address these problems, we propose trust matrix measure in this paper, which combines user similarity with weighted trust propagation. Non cold user passed through different models with trust filter and a cold user generated an optimal score with their preferences for recommendation. Four different recommendation models such as Backpropagation (BPNN) model, SVD (Singular Value Decomposition) model, DNN (Deep Neural Network model) and DNN with Trust were compared to recommend the suitable movie to the user. Results imply that DNN with trust model proved to be the best model with high accuracy of 83% with 0.74 MSE value and can be used for best movie recommendation.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Sasmita Subhadarsinee Choudhury & Alok Kumar Jagadev

  2. Department of Computer Science and Engineering, IcfaiTech, ICFAI Foundation for Higher Education, Hyderabad, India

    Sachi Nandan Mohanty

Authors
  1. Sasmita Subhadarsinee Choudhury
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  2. Sachi Nandan Mohanty
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  3. Alok Kumar Jagadev
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Correspondence to Sasmita Subhadarsinee Choudhury.

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Choudhury, S.S., Mohanty, S.N. & Jagadev, A.K. Multimodal trust based recommender system with machine learning approaches for movie recommendation. Int. j. inf. tecnol. 13, 475–482 (2021). https://doi.org/10.1007/s41870-020-00553-2

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  • DOI: https://doi.org/10.1007/s41870-020-00553-2

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